Last year, we trained a model on customer data. A researcher showed they could reconstruct customer information from model outputs. After implementing privacy-preserving techniques across 10+ projects, I’ve learned how to protect sensitive data while enabling AI capabilities. Here’s the complete guide to privacy-preserving AI. Figure 1: Privacy-Preserving AI Techniques Overview Why Privacy-Preserving AI Matters: […]
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LLM Error Handling: Building Resilient AI Applications
Introduction: LLM APIs fail. Rate limits get hit, servers time out, responses get truncated, and models occasionally return garbage. Production applications need robust error handling that gracefully recovers from failures without losing user context or corrupting state. This guide covers practical error handling strategies: detecting and classifying different error types, implementing retry logic with exponential […]
Read more →The Evolution of .NET: Why Modern C# Development Feels Like a Different Language
If you’ve been writing C# for more than a decade, you’ve witnessed something remarkable: the language you learned in the early 2000s bears only a superficial resemblance to what we write today. Modern C# development feels like a different language entirely. C# Syntax Evolution: 2002 vs 2025 The Transformation Journey When .NET Framework first appeared, […]
Read more →LLM Monitoring and Observability: Metrics, Traces, and Alerts
Introduction: LLM applications are notoriously difficult to debug. Unlike traditional software where errors are obvious, LLM issues manifest as subtle quality degradation, unexpected costs, or slow responses. Proper observability is essential for production LLM systems. This guide covers monitoring strategies: tracking latency, tokens, and costs; implementing distributed tracing for complex chains; structured logging for debugging; […]
Read more →LLM Security Best Practices: Protecting AI Applications from Attacks
Introduction: LLM applications face unique security challenges. Prompt injection attacks can hijack model behavior, sensitive data can leak through responses, and malicious outputs can harm users. Traditional security measures don’t fully address these risks—you need LLM-specific defenses. This guide covers practical security strategies: validating and sanitizing inputs, detecting prompt injection attempts, filtering sensitive information from […]
Read more →Building the Modern Data Stack: How Spark, Kafka, and dbt Transformed Data Engineering
The data engineering landscape has undergone a fundamental transformation over the past decade. What once required massive Hadoop clusters has evolved into a sophisticated ecosystem of specialized tools: Kafka for ingestion, Spark for processing, and dbt for transformation. Modern Data Stack Architecture The Paradigm Shift: Monolithic → Modular The old approach centered around monolithic platforms […]
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